Neuronal Copying of Spike Pattern Generators

Abstract

Event Abstract Back to Event Neuronal Copying of Spike Pattern Generators Daniel Bush1, 2*, Chrisantha Fernando1 and Phil Husbands2 1 Collegium Budapest, Hungary 2 University of Sussex, United Kingdom The neuronal replicator hypothesis proposes that units of selection exist in the human brain and can themselves replicate and undergo natural selection [1]. This process can explain performance in search tasks that require representational re-description, such as insight problems that cannot be solved by existing reinforcement learning algorithms [2]. We have previously proposed two mechanisms by which a process of neuronal replication might operate, allowing either the copying of neuronal topology by causal inference between layers of neurons or the copying of binary activity vectors in systems of bi-stable spiking neurons. Here, we examine a third possibility: that the neuronal machinery capable of producing high fidelity spatio-temporal spike patterns can be copied between cortical regions.Our model is comprised of a spiking, feed-forward neural network with axonal delays that implements spike-timing dependent plasticity (STDP) and synaptic scaling. Initially, input spike patterns to the first layer of neurons are followed – after some short delay - by sub-threshold depolarization in the output layer. If a sufficient richness of axonal delays exists in this feed-forward mapping then the desired transformation can be achieved, as synaptic weights are selectively potentiated according to the correspondence between their axonal delays and the desired input / output firing latencies. We subsequently demonstrate that a wide range of input / output spike pattern transformations, including the replication / identity function, can be learned with only a short period of supervised training. Interestingly, following this initial learning period, synchronous stimulation of the intermediate layer can also produce the desired output spike pattern with high fidelity. Temporal coding offers numerous advantages for processing in spiking neural networks [3], and our model describes a fundamental operation that is likely to be essential for a diverse range of cortical functions. It may be a particularly important component of symbolic neuronal processing [4], as it allows the representation of multiple distinct individual copies of an informational unit. The respective forms of neural stimulation that are utilised in this research – namely, spatio-temporal input and output patterns that repeat cyclically, and synchronous stimulation at low frequencies – also correspond with well-documented cortical activity regimes that appear during waking and sleep, and clear parallels can be drawn between this work and the theory of polychronous groups [5]. In the year of Darwin’s bicentenary, this research aims to provide the foundations for extending the framework of selectionism to the realm of the human brain.